{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 贝叶斯公式：\n",
    "$$P(Y|X)=\\frac{P(X|Y)P(Y)}{P(X)}$$\n",
    "    由以下的联合概率公式推导出来：\n",
    "$$P(Y,X)=P(Y|X)P(X)=P(X|Y)P(Y)$$\n",
    "    其中$P(Y)$为先验概率，$P(Y|X)$为后验概率，$P(Y,X)$为联合概率\n",
    "    \n",
    "           \n",
    "- 从机器学习的角度，把 `X` 理解成“具有某特征”，把 `Y` 理解成“类别标签”，在最简单的二分类时，将 `Y` 理解成“属于某类”的标签。\n",
    "$$P(“属于某类”|“具有某特征”)=\\frac{P(“具有某特征”|“属于某类”)P(“属于某类”)}{P(“具有某特征”)}$$\n",
    "\n",
    "> $P(“属于某类”|“具有某特征”)$=在已知某样本“具有某特征”的条件下，该样本“属于某类”的概率。所以叫做『后验概率』。<br/>\n",
    "$P(“具有某特征”|“属于某类”)$=在已知某样本“属于某类”的条件下，该样本“具有某特征”的概率。<br/>\n",
    "$P(“属于某类”)$=（在未知某样本具有该“具有某特征”的条件下，）该样本“属于某类”的概率。所以叫做『先验概率』。<br/>\n",
    "$P(“具有某特征”)$=(在未知某样本“属于某类”的条件下，)该样本“具有某特征”的概率。\n",
    "\n",
    "\n",
    "     \n",
    "- 垃圾邮件识别，即求 $P(“垃圾邮件”|“具有某特征”)$\n",
    "> `example` = \"我司可办理正规发票（保真）17%增值税发票点数优惠！\"\n",
    "$P(“垃圾邮件”|example) = \\frac{垃圾邮件中出现 example 的次数}{垃圾邮件中出现 example 的次数+正常邮件中出现 example 的次数}$\n",
    "\n",
    "    - 实际情况，邮件中句子的可能性是无限的，特定句子出现的概率非常小；但组成句子的词语却是有限的\n",
    "    > $P(“正常邮件”|(“我”,“司”,“可”,“办理”,“正规发票”,“保真”,“增值税”,“发票”,“点数”,“优惠”) = \\frac{P((“我”,“司”,“可”,“办理”,“正规发票”,“保真”,“增值税”,“发票”,“点数”,“优惠”)|\"正常邮件\")P(“正常邮件”)}{P((“我”,“司”,“可”,“办理”,“正规发票”,“保真”,“增值税”,“发票”,“点数”,“优惠”))}$\n",
    "\n",
    "- 条件独立假设\n",
    "> $P((“我”,“司”,“可”,“办理”,“正规发票”,“保真”,“增值税”,“发票”,“点数”,“优惠”)|“正常邮件”) = P(“我”|“正常邮件”)P(“司”|“正常邮件”)...P(“优惠”|“正常邮件”)$\n",
    "\n",
    "- 贝叶斯方程加上条件独立假设就是朴素贝叶斯方法`(NaiveBayes)`\n",
    "    - 句子概率变成了单词概率的连乘，单词的顺序对结果无影响；朴素贝叶斯失去了单词之间的顺序信息。词袋模型`(bag of words)`\n",
    "    \n",
    "    \n",
    "    \n",
    "- 邮件中重复的词，计算多次概率，**多项式模型**\n",
    "- 将重复的词语都视为其只出现 1 次，**伯努利模型（又称为二项独立模型）**；丢失了词频信息\n",
    "- 计算句子概率时，不考虑重复词语出现的次数；但在统计词语的概率 $P(“词语”|“垃圾邮件”)$，考虑重复词语的出现次数；**混合模型**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns; sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x15a226fe278>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a2262a5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "\n",
    "\n",
    "x,y=make_blobs(100,2,centers=2,random_state=2,cluster_std=1.5)\n",
    "plt.scatter(x[:,0],x[:,1],c=y,s=50,cmap='RdBu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianNB(priors=None)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.naive_bayes import GaussianNB\n",
    "model=GaussianNB()\n",
    "model.fit(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng=np.random.RandomState(0)\n",
    "xnew=[-6,-14]+[14,18]*rng.rand(2000,2)\n",
    "ynew=model.predict(xnew)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.902170524311957, 7.789182875858786, -13.793829460308247, 3.381339464828492)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a22b5b5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0],x[:,1],c=y,s=50,cmap='RdBu')\n",
    "lim = plt.axis()\n",
    "\n",
    "plt.scatter(xnew[:,0],xnew[:,1],c=ynew,s=20,cmap=\"RdBu\",alpha=0.1)\n",
    "plt.axis(lim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89, 0.11],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [1.  , 0.  ],\n",
       "       [0.  , 1.  ],\n",
       "       [0.15, 0.85]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yprob=model.predict_proba(xnew)\n",
    "yprob[-8:].round(2)\n",
    "\n",
    "## 计算每个点各属于哪一类的概率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Multinomial Naive Bayes\n",
    "## Example: Classifying Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "\n",
    "data=fetch_20newsgroups()\n",
    "data.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "categories = ['talk.religion.misc', 'soc.religion.christian',\n",
    "              'sci.space', 'comp.graphics']\n",
    "train = fetch_20newsgroups(subset='train', categories=categories)\n",
    "test = fetch_20newsgroups(subset='test', categories=categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: dmcgee@uluhe.soest.hawaii.edu (Don McGee)\n",
      "Subject: Federal Hearing\n",
      "Originator: dmcgee@uluhe\n",
      "Organization: School of Ocean and Earth Science and Technology\n",
      "Distribution: usa\n",
      "Lines: 10\n",
      "\n",
      "\n",
      "Fact or rumor....?  Madalyn Murray O'Hare an atheist who eliminated the\n",
      "use of the bible reading and prayer in public schools 15 years ago is now\n",
      "going to appear before the FCC with a petition to stop the reading of the\n",
      "Gospel on the airways of America.  And she is also campaigning to remove\n",
      "Christmas programs, songs, etc from the public schools.  If it is true\n",
      "then mail to Federal Communications Commission 1919 H Street Washington DC\n",
      "20054 expressing your opposition to her request.  Reference Petition number\n",
      "\n",
      "2493.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(train.data[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "model=make_pipeline(TfidfVectorizer(),MultinomialNB())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.fit(train.data,train.target)\n",
    "\n",
    "labels=model.predict(test.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(92.68,0.5,'predicted label')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15a1fcce908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "mat=confusion_matrix(test.target,labels)\n",
    "sns.heatmap(mat.T,square=True,annot=True,fmt='d',cbar=False,\n",
    "           xticklabels=train.target_names,yticklabels=train.target_names)\n",
    "plt.xlabel('true label')\n",
    "plt.ylabel('predicted label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_category(s,train=train,model=model):\n",
    "    pred=model.predict([s])\n",
    "    return(train.target_names[pred[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'sci.space'"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('sending a payload to the ISS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soc.religion.christian'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('discussing islam vs atheism')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'comp.graphics'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_category('determining the screen resolution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When to Use Naive Bayes!!!\n",
    "\n",
    "- When the naive assumptions actually match the data (very rare in practice)\n",
    "- For very well-separated categories, when model complexity is less important\n",
    "- For very high-dimensional data, when model complexity is less important"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "p(x)p(y|x)=p(y)p(x|y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 文本分类问题\n",
    "- 新闻主题分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time \n",
    "import random\n",
    "import jieba\n",
    "import sklearn\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "import numpy as np\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 词汇表\n",
    "def make_word_set(words_file):\n",
    "    words_set = set()\n",
    "    with open(words_file, 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            word = line.strip().decode(\"utf-8\")\n",
    "            if len(word) > 0 and word not in words_set:\n",
    "                words_set.add(word)\n",
    "    return words_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文本处理\n",
    "def text_processing(folder_path, test_size=0.2):\n",
    "    folder_list = os.listdir(folder_path)\n",
    "    data_list = []\n",
    "    class_list = []\n",
    "\n",
    "    # 遍历文件夹\n",
    "    for folder in folder_list:\n",
    "        new_folder_path = os.path.join(folder_path, folder)\n",
    "        files = os.listdir(new_folder_path)\n",
    "        # 读取文件\n",
    "        j = 1\n",
    "        for file in files:\n",
    "            if j > 100:  # 怕内存爆掉，只取100个样本文件，你可以注释掉取完\n",
    "                break\n",
    "            with open(os.path.join(new_folder_path, file), 'r') as fp:\n",
    "                raw = fp.read()\n",
    "            ## 是的，随处可见的jieba中文分词\n",
    "            jieba.enable_parallel(4)  # 开启并行分词模式，参数为并行进程数，不支持windows\n",
    "            word_cut = jieba.cut(raw,\n",
    "                                 cut_all=False)  # 精确模式，返回的结构是一个可迭代的genertor\n",
    "            word_list = list(word_cut)  # genertor转化为list，每个词unicode格式\n",
    "            jieba.disable_parallel()  # 关闭并行分词模式\n",
    "\n",
    "            data_list.append(word_list)  #训练集list\n",
    "            class_list.append(folder.decode('utf-8'))  #类别\n",
    "            j += 1\n",
    "\n",
    "    ## 粗暴地划分训练集和测试集\n",
    "    data_class_list = zip(data_list, class_list)\n",
    "    random.shuffle(data_class_list)\n",
    "    index = int(len(data_class_list) * test_size) + 1\n",
    "    train_list = data_class_list[index:]\n",
    "    test_list = data_class_list[:index]\n",
    "    train_data_list, train_class_list = zip(*train_list)\n",
    "    test_data_list, test_class_list = zip(*test_list)\n",
    "\n",
    "    #其实可以用sklearn自带的部分做\n",
    "    #train_data_list, test_data_list, train_class_list, test_class_list = sklearn.cross_validation.train_test_split(data_list, class_list, test_size=test_size)\n",
    "\n",
    "    # 统计词频放入all_words_dict\n",
    "    all_words_dict = {}\n",
    "    for word_list in train_data_list:\n",
    "        for word in word_list:\n",
    "            if all_words_dict.has_key(word):\n",
    "                all_words_dict[word] += 1\n",
    "            else:\n",
    "                all_words_dict[word] = 1\n",
    "\n",
    "    # key函数利用词频进行降序排序\n",
    "    all_words_tuple_list = sorted(all_words_dict.items(),\n",
    "                                  key=lambda f: f[1],\n",
    "                                  reverse=True)  # 内建函数sorted参数需为list\n",
    "    all_words_list = list(zip(*all_words_tuple_list)[0])\n",
    "\n",
    "    return all_words_list, train_data_list, test_data_list, train_class_list, test_class_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def words_dict(all_words_list, deleteN, stopwords_set=set()):\n",
    "    # 选取特征词\n",
    "    feature_words = []\n",
    "    n = 1\n",
    "    for t in range(deleteN, len(all_words_list), 1):\n",
    "        if n > 1000:  # feature_words的维度1000\n",
    "            break\n",
    "\n",
    "        if not all_words_list[t].isdigit() and all_words_list[\n",
    "                t] not in stopwords_set and 1 < len(all_words_list[t]) < 5:\n",
    "            feature_words.append(all_words_list[t])\n",
    "            n += 1\n",
    "    return feature_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 文本特征\n",
    "def text_features(train_data_list, test_data_list, feature_words, flag='nltk'):\n",
    "    def text_features(text, feature_words):\n",
    "        text_words = set(text)\n",
    "        ## -----------------------------------------------------------------------------------\n",
    "        if flag == 'nltk':\n",
    "            ## nltk特征 dict\n",
    "            features = {\n",
    "                word: 1 if word in text_words else 0\n",
    "                for word in feature_words\n",
    "            }\n",
    "        elif flag == 'sklearn':\n",
    "            ## sklearn特征 list\n",
    "            features = [\n",
    "                1 if word in text_words else 0 for word in feature_words\n",
    "            ]\n",
    "        else:\n",
    "            features = []\n",
    "        ## -----------------------------------------------------------------------------------\n",
    "        return features\n",
    "\n",
    "    train_feature_list = [\n",
    "        text_features(text, feature_words) for text in train_data_list\n",
    "    ]\n",
    "    test_feature_list = [\n",
    "        text_features(text, feature_words) for text in test_data_list\n",
    "    ]\n",
    "    return train_feature_list, test_feature_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 分类，同时输出准确率等\n",
    "def text_classifier(train_feature_list,\n",
    "                    test_feature_list,\n",
    "                    train_class_list,\n",
    "                    test_class_list,\n",
    "                    flag='nltk'):\n",
    "    ## -----------------------------------------------------------------------------------\n",
    "    if flag == 'nltk':\n",
    "        ## 使用nltk分类器\n",
    "        train_flist = zip(train_feature_list, train_class_list)\n",
    "        test_flist = zip(test_feature_list, test_class_list)\n",
    "        classifier = nltk.classify.NaiveBayesClassifier.train(train_flist)\n",
    "        test_accuracy = nltk.classify.accuracy(classifier, test_flist)\n",
    "    elif flag == 'sklearn':\n",
    "        ## sklearn分类器\n",
    "        classifier = MultinomialNB().fit(train_feature_list, train_class_list)\n",
    "        test_accuracy = classifier.score(test_feature_list, test_class_list)\n",
    "    else:\n",
    "        test_accuracy = []\n",
    "    return test_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print \"start\"\n",
    "\n",
    "## 文本预处理\n",
    "folder_path = 'datasets/SogouC/Sample'\n",
    "all_words_list, train_data_list, test_data_list, train_class_list, test_class_list = text_processing(\n",
    "    folder_path, test_size=0.2)\n",
    "\n",
    "# 生成stopwords_set\n",
    "stopwords_file = 'datasets/stopwords_cn.txt'\n",
    "stopwords_set = make_word_set(stopwords_file)\n",
    "\n",
    "## 文本特征提取和分类\n",
    "# flag = 'nltk'\n",
    "flag = 'sklearn'\n",
    "deleteNs = range(0, 1000, 20)\n",
    "test_accuracy_list = []\n",
    "for deleteN in deleteNs:\n",
    "    # feature_words = words_dict(all_words_list, deleteN)\n",
    "    feature_words = words_dict(all_words_list, deleteN, stopwords_set)\n",
    "    train_feature_list, test_feature_list = text_features(\n",
    "        train_data_list, test_data_list, feature_words, flag)\n",
    "    test_accuracy = text_classifier(train_feature_list, test_feature_list,\n",
    "                                    train_class_list, test_class_list, flag)\n",
    "    test_accuracy_list.append(test_accuracy)\n",
    "print test_accuracy_list\n",
    "\n",
    "# 结果评价\n",
    "#plt.figure()\n",
    "plt.plot(deleteNs, test_accuracy_list)\n",
    "plt.title('Relationship of deleteNs and test_accuracy')\n",
    "plt.xlabel('deleteNs')\n",
    "plt.ylabel('test_accuracy')\n",
    "plt.show()\n",
    "#plt.savefig('result.png')\n",
    "\n",
    "print \"finished\""
   ]
  }
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